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More reliable insights through advanced data processing

We’re using machine learning for quality assurance and leverage advanced Bayesian statistics to ensure the highest possible data quality.

Illustration showing data processing through machine learning, enhancing the accuracy of insights for brand tracking.
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How does Latana process raw data?

step 1

ML-based quality assurance
We’ve developed an unsupervised learning model to identify and clean out unreliable answers.

step 2

Every bit of information
Partial completes enable us to enhance the accuracy of the dataset.

step 3

Bayesian modelling
Multilevel regression with post stratification (MRP) helps us reduce the margins of error for each datapoint.

Quality assurance

Using machine learning for quality assurance

Accidental or inattentive clicks can happen - but they shouldn’t make it into the dataset. We’ve developed an unsupervised learning model based on dozens of active and passive data points to identify and filter out unreliable responses.

Latana survey on mobile phone showing machine learning in data processing
Image showing the use of partial completes in data processing

Data accuracy

Enhancing the accuracy of the results using partial completes

Partial responses can contribute valuable insights and improve the overall accuracy of the dataset, for example, by highlighting links between gender and brand awareness. We use partial completions to check and ensure that all correlations are as accurate as possible.

Precision of results

Enhancing the precision of each datapoint

We enhance precision through Bayesian time series modelling. For each brand, we feed multiple waves of data into our probabilistic models, employing regression and poststratification techniques to stabilise KPIs and correct for potential sample composition effects. This method ensures accurate estimates even for niche segments, leading to up to 90% lower margins of error compared to industry standards.

Graph showing margin of error in Latana data processing

How does Latana's data processing compare to competitors?

Compare features

Competitors

Latana

Accuracy of niche segments (MoEs)
Up to 20%
<2%
Incidence rate limit of combined segments (eg high-income women under 35 with children)
5%
0.1%
Automatic sample composition stabilisers
None
Up to 5

Experience the power of precise insights

Discover how Latana's advanced data collection and analysis techniques can elevate your brand strategy. From comprehensive surveys to sophisticated algorithms, our platform provides the clarity you need to make confident, data-driven decisions.

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